Automated safety features for motor vehicles have been proposed and developed in recent years. Vision based lane tracking systems have been designed that are supposed to monitor a vehicle position by imaging a roadway and detecting lane markers. These lane tracking systems can be used for lane departure warning, or in more advanced systems may even be used for lane keeping assistance or automated vehicle guidance systems. In such lane tracking systems, a camera captures images of a roadway in front of a vehicle and imaging processing software identifies the lane markers from the roadway. The vision system can then determine the vehicle position relative to the lane markers, for displaying vehicle positioning information to a driver, warning a driver of an unintended lane departure, detecting driving patterns such as those indicative of a drowsy driver, or use in a collision warning/avoidance system. Conventional image processing systems are advancing in capabilities and provide an opportunity for improving vehicle occupant safety and vehicle guidance.
For such lane tracking systems to be effective for vehicle occupant safety, vehicle guidance or other applications, it is important that the lane tracking be effective under most if not all conditions that are encountered in real world applications. However, this is very difficult in practice. For example, a variety of lighting conditions commonly occur that make it much more difficult for an imaging system to accurately determine lane markers and vehicle position. One example of a common difficult imaging condition is bright sunlight where the glare off a tar seam can emit a brighter image than a true lane marker. These common conditions can continue for a considerable time, necessitating lane tracking systems to consider various road conditions.
Prior approaches to lane imaging under complicated but common lighting conditions have attempted to increase the sophistication of the imaging devices. Sophisticated cameras and sophisticated image processing algorithms have been proposed to increase the ability of the imaging system to detect the lane markers despite the poor image quality. Such approaches to solving the problem are complex and have proven costly, in both design and implementation.
One-dimensional detection of lane marker-like features must take into account complicating real world considerations including shadows, skid marks, patching or paving seams, faded markers, yellow on concrete, low sun angles, etc. Accordingly, a need exists for accurate lane marker detection and lane fitting in a roadway under a variety of complicated conditions including lighting conditions, road clutter/marks and irregular lane markers, for vehicle occupant safety and other applications.